| # TwoLeg training |
|
|
| RL training for the TwoLeg biped, built on [mjlab](https://github.com/mujocolab/mjlab) |
| (MuJoCo Warp + rsl_rl). This is a small, self-contained first pass modelled on |
| [`microduck_rl`](../../microduck_rl): one velocity (walking) task, flat ground, |
| no sim2real domain-randomization stack yet. |
| |
| ## Task |
| |
| `Mjlab-Velocity-Flat-TwoLeg` — track a slow twist command: |
| |
| | command | range | |
| |---|---| |
| | forward velocity `lin_vel_y` | `0.0 … 0.3` m/s | |
| | lateral `lin_vel_x` | `-0.1 … 0.1` m/s | |
| | yaw rate `ang_vel_z` | `-0.4 … 0.4` rad/s | |
| |
| Forward is `lin_vel_y`: this model faces `+y`, its left-right axis is `+x` |
| (feet at `x = +-0.034` with equal `y`, foot box 12.4 cm along `y`), and mjlab |
| compares the command to body-frame velocity channel by channel. |
| |
| Rewards: linear/angular velocity tracking, upright torso, joint posture |
| (standing vs walking), joint-limit and action-rate penalties. All 15 joints are |
| actuated by the XML `<position>` servos. |
| |
| ## Robot model |
| |
| `robot_item/chest_both_arms_legs.xml` plus the `chest_move` rig is the source |
| of truth for the mechanism. It is a world-pinned puppet, not a free body, so |
| `scripts/generate_twoleg_model.py` rebuilds it into one connected tree and |
| writes both consumers: |
|
|
| | output | used by | meshdir | |
| |---|---|---| |
| | `../twoleg_mjcf/robot_twoleg.xml` | viewing | `../robot_item` | |
| | `assets/twoleg.xml` | mjlab | `../../robot_item` | |
|
|
| The generator re-parents the limb chains under the torso, recomputes every |
| body's `pos` and `quat` from its world pose, drops the duplicate waist hinges, |
| and carries the geoms over untouched so mass and inertia follow. Regenerate |
| after any change to the reference: |
|
|
| ```bash |
| uv run python training/scripts/generate_twoleg_model.py |
| ``` |
|
|
| Never hand-edit either output. Verify the result against the reference: |
|
|
| ```bash |
| uv run python training/scripts/check_model_vs_reference.py |
| ``` |
|
|
| It should report 121 bodies, 262 geoms, a mass ratio of 1.000, and 0.00 mm on |
| every shared joint anchor. |
|
|
| ## Quickstart |
|
|
| Requires a CUDA GPU (mjlab runs through MuJoCo Warp) and [uv](https://docs.astral.sh/uv/). |
|
|
| ```bash |
| cd training |
| uv sync |
| |
| # train |
| uv run train Mjlab-Velocity-Flat-TwoLeg --env.scene.num-envs 4096 |
| |
| # watch a checkpoint |
| uv run play Mjlab-Velocity-Flat-TwoLeg --checkpoint <path/to/model.pt> |
| ``` |
|
|
| Logs go to `logs/rsl_rl/velocity/<timestamp>/` (TensorBoard). Scale |
| `--env.scene.num-envs` and `--agent.max-iterations` to taste. |
|
|
| ## Layout |
|
|
| ``` |
| training/ |
| ├── assets/twoleg.xml # GENERATED, mjlab-ready model |
| ├── scripts/generate_twoleg_model.py # rebuilds the model from the reference |
| ├── scripts/check_model_vs_reference.py # verifies it against the reference |
| ├── src/twoleg_training/ |
| │ ├── robot/twoleg_constants.py # EntityCfg, HOME pose, XML actuators |
| │ └── tasks/ |
| │ ├── __init__.py # task registration (mjlab.tasks entry point) |
| │ ├── curriculum.py # air-time window ramp |
| │ └── twoleg_velocity_env_cfg.py # env cfg + PPO config |
| └── pyproject.toml |
| ``` |
|
|
